Signal to noise improvement

ABSTRACT

Systems, methods, and processor executable code for high quality wide-range multi-layer image compression of a sequence of video images. A computer-implemented method of adding signal-to-noise improvement layers which are targeted to specific aspects of a sequence of images. The method comprises selecting one or more aspects of a coded image to improve, isolating those image aspects using the selection criteria, and coding a signal-to-noise layer using the isolated coded image aspects. A computer-implemented method for generating a signal-to-noise correction layer from a digitized video image represented as pixels. The method comprises quantizing and dequantizing the digitized video image to create a coded image, subtracting the coded image from the digitized video image to create a signal-to-noise difference image represented as pixels, and applying one or both of quantization weighting and pixel weighting to the signal-to-noise difference image pixel values such that larger difference image pixel values have smaller quantized values.

RELATED APPLICATIONS

This application is a divisional application of application Ser. No.13/772,262, filed Feb. 20, 2013, now U.S. Pat. No. 8,666,184 which is adivisional application of application Ser. No. 13/024,242, filed Feb. 9,2011, now U.S. Pat. No. 8,401,315, which is a divisional application ofSer. No. 11/225,665, filed Sep. 12, 2004, now U.S. Pat. No. 7,916,952,which claims priority to provisional patent application 60/609,773,filed Sep. 14, 2004.

BACKGROUND OF THE INVENTION

This invention relates to compression of images, particularly sequencesof digitized color video images.

The JPEG-2000 Bi-Orthogonal 9/7 Discrete Wavelet Transform

For two decades, band-split technologies such as sub-band coding,low-pass/high-pass split pairs, and wavelet sub-band codings, have beenapplied to image compression. Recently notable is the sub-band discretewavelet transforms (DWT) used in JPEG-2000 (see, for example, “JPEG2000,Image Compression Fundamentals, Standards, and Practice” by David S.Taubman and Michael W. Marcellin, Kluwer Academic Publishers 2002). TheJPEG-2000 still image and intra-coded (i.e., no motion compensation)moving image coding system supports two “bi-orthogonal” wavelet classesin a sub-band configuration. A DWT 5/3 bi-orthogonal subbandconfiguration is used for lossless compression, when exact bit match isrequired, but with only a small amount (typically 2.2:1) of compression.A DWT 9/7 bi-orthogonal subband configuration is more generally useful,and can provide a transform coding method for higher compression ratios,while preserving the “visual essence” of the image (although notbit-exact).

The fundamental merit of the DWT 9/7 bi-orthogonal subband configurationis the resemblance to a low-pass/high-pass filter pair. The“bi-orthogonality” refers to odd and even sample locations using low andhigh pass filters, respectively. This structure is then split into 4sub-bands in JPEG-2000, with low horizontal and vertical (“low-low”),high horizontal and low vertical (“high-low”), low horizontal and highvertical (“low-high”), and high vertical and horizontal (“high-high”)subbands. This subband configuration can also utilize other band-splitfilter sets, and need not be structured bi-orthogonally at even/oddpixels. Any defined low band up-filter and high-band sum (with optionalhigh-band filter) can yield a band-split suitable for use in compressioncoding.

FIG. 1 is a block diagram of a prior art 9/7 DWT bi-orthogonal subbandcompression system in accordance with the teachings of JPEG-2000. Ahigher resolution image 100 to be compressed (or a previous higher layerlow-low subband image) is filtered down by a low band filter 112 (shownas having 9 taps) applied to odd pixels and a high band filter 114(shown as having 7 taps) applied to even pixels, generating 4 subbandimages 120. These analytical filters 112, 114 are first applied in ahorizontal pass, creating intermediate horizontal low and horizontalhigh subbands. These two intermediate subbands are then filtered in avertical pass. Vertically filtering the horizontal low subband with thesame analytical filters 112, 114 results in a low-low subband and ahigh-low subband. Vertically filtering the horizontal high subband withthe same analytical filters 112, 114 results in a low-high subband and ahigh-high subband. During synthesis of an image from the 4 subbands, alow band filter 122 (shown as having 7 taps) is applied to odd pixelsand a high band filter 124 (shown as having 9 taps) is applied to evenpixels.

Band-split low-pass/high-pass filter pairs are most effective inseparating spatial frequency energy. The bi-orthogonal DWT 9/7 issufficiently similar to a low-pass/high-pass filter pair that itfunctions effectively. For idealized samples, the optimal low-passfilter is a truncated sine function (i.e., sinc(x)=sin(x)/x) with thedistance between the filter center and the first zero crossing beingequal to the low-pass pixel spacing. For an octave (factor of tworeduction in resolution) band-split, the spacing from filter center tothe first zero crossing (in both directions) is 2.0 in source resolutionunits, and 1.0 in the half-octave result resolution units. The low-passfilter of the DWT 9/7 roughly resembles this octave truncated sinc,although its dimensions and weights differ somewhat. While idealizedlinear samples never occur in practice, they form the basis of imageprocessing theory, such as Nyquist sampling and filtering. Note thattheory uses a sinc of infinite extent, which can be truncated in actualpractice. A truncated sinc is not ideal according to filter theorybecause it is truncated and typically non-linear, and because thesamples are not ideally filtered when they are created or reconstructed.However, a truncated sinc is as close as possible to optimal in mostimage filtering applications.

Quantization

A part of most image compression coding is the use of quantization. A“quantization parameter”, often known by its initials “QP”, is dividedinto localized frequency coefficients in essentially every common typeof non-lossless compression system. To reconstruct a compressed image,the frequency coefficients are re-multiplied by the appropriatequantization parameter. Because of the integer nature of the quantizedvalues, the reconstructed coefficients with vary by ±half of the valueof a step in the quantization parameter. For example, if thequantization parameter is 6, the reconstructed value will typically vary±3. Further, in order to increase the number of zero coefficients, whichcode most efficiently, a “deadband” is usually applied around zero.Thus, for example, even with a quantization parameter of 6, the value of0 in a coefficient may span the range of ±6 (rather than ±3 without anydeadband).

In JPEG-2000, the quantization parameter may be specified for eachsubband with a single floating-point value. The JPEG-2000 deadband isfixed at double the width of the quantization step. JPEG-2000 also usesbit truncation methods to reduce coded bits which remove some smallquantized values, even though the quantization was non-zero. Because ofthis, JPEG-2000 does not compress only with quantization, but also withthe coded location of bits, often resulting in a relatively randomadditional error, above and beyond quantization error.

Coefficient Coding Structure

The coding of frequency coefficients is typically a lossless processinvolving some fixed structure and a variable-length coding (VLC) method(such as Huffinan or arithmetic coding). It is typical for thecoefficient coding structure to match the transform. For example, inMPEG-2, the structure of the coefficient coding is identical to theDiscrete Cosine Transform (DCT) 8×8 pixel block. A pattern of variablelength code is applied to the order of values in an 8×8 block, such aszig-zag from the corner, or a left-to-right, top-to-bottom scan. InJPEG-2000, the DWT 9/7 is coded up from the root coefficient or bottomresolution to each four-fold expansion of coefficients creating thesub-bands (low-low, high-low, low-high, and high-high). The coefficientsare then synthesized into the next higher resolution, and become thelow-low subband of the next layer up.

Variable Length Coding

Variable length codes used in image compression range from extremelysimple, such as run-length codes and delta codes, to moderately complexsuch as arithmetic codes. The purpose of the variable length code is toreduce the number of bits necessary to code the coefficient valuescompared to using a fixed number of bits capable of coding the maximumrange. For example, if 16 bits are used because the values can rangebetween ±32767, but only a few values are larger than ±127, then 8-bitscould be used with one “escape” code reserved to indicate that the nextvalue needs an additional 16-bits. Although the large “escaped” valuethen needs 24 bits (8+16), it is usually infrequent enough that theaverage coefficient coding size will be nearer to 8-bits than 24-bits.This methodology can be extended based upon the principle that verysmall values, and even zero itself, are much more likely than largervalues of any size. In this way, a Huffman table attempts to use theshortest codes for small and likely values, and gradually longer codesfor larger and less likely values.

The arithmetic coding methodology allows multiple code values to becoded together, resulting in codes which have non-integer numbers ofaverage bits for each coefficient code value. For example, two valuesmay be coded with 7 bits, such that each code value uses the equivalentof 3½ bits each.

It is typical in compression systems such as MPEG-2 and JPEG-2000 tocombine run-length, delta, and Huffman codes.

Motion Compensation

JPEG-2000 does not offer motion compensation, since every frame standsalone. This is known as “intra” coding. MPEG-2, and many other similarcoding systems, offer motion compensation, using blocks and motionvectors, for “inter” coding of images in a sequence of images. In suchmotion compensated coding systems, it is common practice to structurethe motion blocks as a superset of the transform coding blocks. Forexample, in MPEG-2, the motion blocks are typically 16×16 pixels in size(16×8 for interlace), which encompasses four 8×8 DCT blocks (two forinterlace). Thus, the block motion compensation structure is closelyfitted to the DCT transform coding structure. MPEG-4, both as part 2(original MPEG-4 video) and part 10 (also called the “Advanced VideoCoder”), are structured similarly to MPEG-2 in these aspects.

Spatial Scalability

MPEG-2 offers a rarely used “spatial scalable” option which allows anadditional resolution increasing layer to be coded. The up-filter forthis option differs greatly from the theoretically optimal truncatedsinc. MPEG-2 also offers signal-to-noise-reduction (SNR) scalability,which is also rarely used. The basic structure of the SNR level ofMPEG-2 is identical to basic MPEG-2 —summing a correction to improvesignal to noise in the resulting image. Neither spatial scalability norSNR scalability are targeted at any specific goals, only generalincrease in resolution and SNR, respectively. Only a single SNR and asingle spatial scalability level are defined in MPEG-2.

JPEG-2000 offers the ability to prioritize and compartmentalize theorder of bits in an image, such that early termination of decoding ispossible (if the image is encoded with prioritization and/orcompartmentalization). This allows a method of scalability for eitherSNR or resolution enhancement, but is limited by the bit-plane orderedcoding and block-region compartmentalization properties of JPEG-2000.For example, transformed pixels of higher priority can be pre-shiftedleft during quantization (thus scaling by powers of two), and un-shiftedduring decoding, to provide a limited form of SNR scalability (limitedsince a left shift scaling must be a power of two). All highestsignificance (most significant bit) bit planes (within a tile partitionor other regional compartment) are decoded first, then the next highestbit, etc., until the decoder truncates prior to decoding all of theavailable bits, or until all coded bits have been decoded. This methodof ordered coding and optional pre-shifting allows some spatial and SNRscalability, but is limited to stopping within the boundaries a specificpre-ordered bit plane. Thus, the scalability available within JPEG-2000is limited to bit-planes sharing a common QP, which are by their natureseparated by a factor of two in significance. Finer granularity ofscalability is not possible in JPEG-2000.

Floating Point

It has been common practice to mix floating-point and integercomputations in reference compression coder software. For example,MPEG-2 and MPEG-4 use floating point reference DCT implementations, butinteger processing for color processing, motion compensation, and mostother aspects of the coding systems. JPEG-2000 uses a combination ofinteger and floating-point processing in its reference implementation.MPEG-4 part 10 uses an “integer transform” which combines thequantization and DCT transform steps into a single integer operation.Although the MPEG-4 part 10 implementation is not bit-exact invertible,the integer decoding is intended to exactly match between the encoderand decoder. This is a design feature of motion-compensated codingsystems which the current inventor (along with David Ruhoff) has filedas patent application number 20020154693, entitled “High PrecisionEncoding and Decoding of Video Images”. The use of “exact match”decoding (that is, exactly matching between the decoder portion of theencoder, and all bitstream decoders) allows limited precision integercomputations to be used without propagating errors when using motioncompensation.

Some integer processing has been an essential ingredient of most if notall previous compression coding systems. This has been intentional,since floating-point computation has usually been substantially slowerthan integer computation, especially 16-bit and 8-bit integercomputation.

OpenExr

Relatively recently, Lucasfilm Industrial Light and Magic (a digitalspecial-effects production company) and Nvidia (a maker of video cardsfor personal computers) have teamed up to create a standard known as“OpenExr”. OpenExr is an open “Extended Range” floating pointrepresentation featuring a 16-bit floating point representation having asign bit, a 5-bit exponent, and a 10-bit mantissa. This representationprovides sufficient precision for most image processing applications, aswell as allowing an extended range for white and black. The 16-bit“half” floating representation provided by OpenExr can be directlymapped to standard 32-bit IEEE floating point representation for easyinteroperability.

It is common practice to display pixel values with black at zero, andwhite at the maximum integer value, or at a floating value of 1.0.However, digital image masters, especially those involving computergraphics, often need to represent a wider range of white and dark thanis available with 8-bit or 10-bit integers having black at 0 and whiteat 255 or 1023, respectively. OpenExr allows white values and blackvalues to extend substantially beyond this range. Further, concatenatedcomputations will have higher resulting precision when usingOpenExr16-bit floating point representation, or 32-bit floating pointrepresentation, than integer computations (even when using integercomputations with exact-match decoding).

OpenExr has the further benefit of allowing direct representation oflinear light values, rather than requiring a non-linear (usually avideo-gamma exponent) representation when using integers for pixelvalues.

OpenExr also offers an optional lossless compression coder (usuallyyielding 2:1 compression). This compression coder is based upon acombination of the simple Haar difference wavelet, a reduced-precisionclustering table, and a Huffman variable-length code. Thereduced-precision clustering table increases compression if many of thecode values are not used. For example, such is the case if convertingfrom 10-bit integer pixel values, since only 1023 codes (of the possible65536) codes would be used.

SUMMARY OF THE INVENTION

The invention encompasses systems, methods, and computer programs forhigh quality wide-range multi-layer image compression coding.

One aspect of the invention is the consistent ubiquitous use of 32-bitfloating point for pixel and transformed pixel values in allcomputations, except for initial RGB (red-green-blue channels) imagefile input and at the quantization step itself. This aspect of theinvention can be extended to 16-bit OpenExr floating point, to 24-bitfloating point, to 48-bit, 64-bit, 128-bit, and all other usefulfloating point computational formats.

Another aspect of the invention applies an adjustable floating-pointdeadband, via subtraction and zero-floor during encoding, and viaaddition during decoding.

Another aspect of the invention is the preservation of the inherent widerange of floating point representation throughout compression. Thisincludes values above 1.0, very small values, and negative values. Oneof the most significant practical benefits of this aspect of theinvention is the ability to efficiently compress wide-range moving andstill image data, beyond the brightness value implicit in the selectednumeric representation of the final displayed reference white.

Another aspect of the invention is to use an optimal band-split filterphase construction in which low-pass bands are created in between pixelsby appropriate selection of filter tap weights. The benefits of the moreoptimal band-split filter structure also apply to SNR improvementlayers, at any resolution level.

Another aspect of the invention uses entire SNR layers at lowerresolution levels. This aspect of the invention extends the notion ofhaving the SNR resolution layer at the final resolution by allowingmultiple SNR constructions. The use of the optional low-low band in aDWT 9/7 bi-orthogonal sub-band wavelet for the SNR layer is one suchnovel SNR structure. The use of optional switching between the moreoptimal band-split filter and the DWT 9/7 bi-orthogonal wavelet, basedupon least bits with a common quantization, is another novel aspect ofthis invention. This aspect of the invention further extends theseconcepts to having multiple SNR layers at the full resolution level orat any lower resolution level.

Another aspect of the invention is the targeting of specific SNR layersto specific quality improvements. For example, one SNR layer can beapplied to details in the image, using a high-pass or band-pass filterto select specific spatial frequencies for that SNR layer. Further,multiple pass-bands can be summed, such that a particular SNR layer canimprove several bands simultaneously.

Another aspect of the invention is to concentrate coding bits in regionsof interest in targeted band-split and SNR layers by decreasing theamplitude of pixel differences or high bands near the image frame edgesusing a weighting which decreases, possibly to zero, as a function ofnearness to the frame edge. In the high-frequency bands, the decreasecan result in decreased sharpness and detail. In SNR bands, the decreasecan result in less accuracy, clarity, and even less noise, at the frameedges.

Another aspect of the invention is to establish a collection ofstatically-assigned targets for high-pass layers and/or for SNR layers.In this way, during decoding, whichever regions are of greatest interestcan be decoded, while those of lower interest need not be decoded. Thus,numerous regional enhancement layers (both high-band and/or SNR) can beprovided, allowing detailed decoder focusing on specific regional areas.

Another aspect of the invention improves the SNR by using a lowerquantization value (better precision) for regions of the image showing ahigher difference (compression coding error) from the original (for thatresolution layer).

Another aspect of the invention is optional application of non-linearfunctions of R, G, and B, or of Y, U, and V, such as gamma or log, whencomputing the difference values when creating an SNR layer.

Another aspect of the invention is to improve layered transformquantization by utilizing finer overall quantization (by using lowerquantization parameter values) at lower resolution levels, inconjunction with regional quantization scaling. Since the lower levelsare at a lower resolution, each pixel in a lower level can affect anumber of pixels in higher layers.

Another aspect of the invention to characterize and remove source imagefixed-pattern noise, when such characterization is possible, prior tomotion-compensated compression. Another aspect of the invention is theprocess whereby fixed pattern noise is reduced or removed before usingother technologies to steady the scanned film image, prior tocompression with this invention.

Another aspect of the invention is the use of one or more full-range lowbands, meaning that at least the lowest band of a band-split hierarchyutilizes floating-point representation to provide wide-range and highprecision.

Another aspect of the invention is the use of alternate quantizationcontrol images for SNR bands and other high resolution enhancing bands.As another optional aspect of this invention, however, if a coarserquantization is used at one or more required or optional layers (forhigher compression at those layers), then it can sometimes be beneficialto dither the pixel values with pseudo-random noise prior toquantization.

Another aspect of the invention is to apply the lossless variable-lengthcoding of band-split transform pixel coefficients using adaptiveregions. These regions can have arbitrary size and shape, and areselected solely on lossless coding efficiency (i.e., minimum bit-size).Another aspect of the invention applies adaptive coding region size,shape, and variable-length coding tables to low resolutionfloating-point low bands or floating-point regional quantization images.

Another aspect of the invention is the use of a file structure for thelayers of bits. Each resolution layer, each SNR layer, eachfloating-point lowest band, each quantization regional low band, and themotion compensation form different aspects of the bits generated duringcompression. The compressed bits are organized into files, with one filefor each frame for each aspect of that frame. Each aspect is furtherplaced in a folder dedicated to that aspect. The overall project orscene contains all of these aspect folders within a highest-level folderor storage device.

Another aspect of the invention is an optional method of inserting newintra frames by counting the number of bits needed for a motioncompensated frame. If that number is not at least a reasonable percentless than the bits needed for the previous intra frame, then themotion-compensated frame can be discarded and an intra frame can bemade.

Novel and useful aspects of the invention include at least thefollowing:

(1) Use of a different quantization parameter value at two or moreresolution levels of a coding transform resolution hierarchy, includinguse of reduced-resolution levels of a band-split or band-split hierarchyas regional scaling for quantization.

(a) Optionally applied to the bi-orthogonal subband 9/7 Discrete WaveletTransform resolution hierarchy.

(b) Optionally applied to a more optimal band-split hierarchy.

(c) Optionally including a minimum and/or maximum on thereduced-resolution quantization scale, optionally as a function of eachresolution level.

(2) Use of a floating-point quantization parameter value in conjunctionwith integer or floating point reduced-resolution levels of a band-splitor band-split hierarchy as regional scaling for the floating-pointquantization.

(3) Use of high precision, such as floating-point orhalf-floating-point, for a low resolution level of a coding transformhierarchy.

(a) Optionally including the bi-orthogonal subband 9/7 Discrete WaveletTransform resolution hierarchy.

(4) Use of floating point everywhere in an image compression codingbetween input (pixel input is optionally integer), up to the point ofthe quantization divide (after which coding is bit-exact lossless).Thus, all transform processing is performed in floating point.

(5) Similar use in the decoder of floating point everywhere, after thedequantization multiply, up to the final pixel values (which may beoptionally converted to integers as a final step).

(6) Use of actual image (intra-style) low bands for quantization, butapplying quantization not only to intra-coding, but also (when motioncompensated) to motion-compensated difference bands (including bands atdifferent levels, higher and/or lower, of the band-split hierarchy).

(7) Use of a resolution hierarchy, wherein the minimum value is takenfor a suitable low-low band, for use as a scale factor for thequantization parameter for all pixels at higher resolution levels;

(a) Optionally including use of specific minimum and/or maximum values,optionally as a function of each resolution level.

(b) Optionally including adjacent pixel overlap at each level, to takeinto account the influence of the band-split filter extent.

(8) Use of resulting coding error difference values to create a newsuitable low-low band for use in weighting quantization for an SNRcorrection layer such that larger errors have smaller quantizationparameter values (thus higher number of correction bits generated).

(a) Optionally including adjacent pixel overlap at each level, to takeinto account the influence of the band-split filter extent.

(b) Optionally including an additional quantization weighting as afunction of the amount of factor that a given pixel's error is above theaverage error

(9) Use of a different deadband quantization zone at two or moreresolution levels of a coding transform resolution hierarchy.

(a) Optionally applied to the bi-orthogonal subband 9/7 Discrete WaveletTransform resolution hierarchy.

(10) Use of noise dithering at the point of quantization, with the noiselevel being set at the quantization stepsize or smaller. When setsmaller, the goal is for the sum of the inherent image noise (at thatlevel) plus the added noise, to equal the quantization step. Thedeadband (around zero) noise dithering may optionally be treateddifferently than other steps.

(11) Use of a statistical numeric characterization, such as a transformamplitude histogram as a function of one or more of color, brightness,and region for one or more SNR layers in order to reproduce theappearance of film grain or camera noise.

(12) Use of the count of bits generated by a motion compensated frame,in relative comparison to the count of bits within the previous intraframe, to determine whether to recompute the motion compensated frame asan intra frame instead.

(13) Use of adaptive region sizes to optimize for minimum bits whenlossless bit coding using one or more choices of variable-length codingtables.

(14) Use of an optional intermediate file folder structure to groupcompressed data components for various layers.

The details of one or more implementations of the invention are setforth in the accompanying drawings and the description below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a prior art 9/7 DWT bi-orthogonal subbandcompression system.

FIG. 2 is a block diagram showing application of an optimaltwo-dimensional spatial band-split filter in accordance with one aspectof the invention, using a factor of two in this example.

FIG. 3 is a diagram of a row of original pixels showing the coincident(with respect to the pixels) phase construction for the DWT 9/7structure.

FIG. 4 is a diagram of a row of original pixels showing an intermediate(with respect to the pixels) phase construction for the presentinvention.

FIG. 5 is a block diagram showing one implementation of an imagecompression system having a resolution and SNR layered structure, withoptional SNR layers at various resolutions.

FIG. 6 is a block diagram showing one example of an image compressionsystem having layered compression processing.

FIG. 7 is a diagram of a source image showing a scene suitable forgenerating targeted layers.

FIG. 8 is a diagram showing one possible layered file structure inaccordance with one aspect of the invention.

FIG. 9 is a block diagram showing one implementation of various aspectsof the invention in a non-motion compensated compression/decompressionsystem.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION OF THE INVENTION

Ubiquitous Floating Point

As image compression processing becomes more complex, as it does withthe many-layered DWT 9/7 subband transform, the number of concatenatedcomputational steps increase substantially, and may be on the order ofthousands of processing steps. The common practice of mixing integer andfloating point processing becomes problematic because the quantizationerrors from a sequence of computation steps can accumulate tosignificant errors. One aspect of the present invention is theconsistent ubiquitous use of 32-bit floating point for pixel andtransformed pixel values in all computations, except for initial RGB(red-green-blue channels) image file input and at the quantization stepitself. In the preferred embodiment, the image input options include32-bit floating point (e.g., “Tiff”, “DPX”, or “OpenExr” file formats),16-bit “half” floating-point OpenExr, 16-bit integer Tiff, and 10-bitDPX (for compatibility) file formats. In the preferred embodiment,output format options for decoded images include all of the above except10-bit DPX. All of these formats, if they are not already 32-bitfloating point, are immediately converted to RGB 32-bit floating-pointvalues upon input. The ubiquitous use of 32-bit floating point allowsmore complex computations to be concatenated during encoding anddecoding without accumulating significant error. The error can be madeinsignificant in comparison to the quantization error inherent inquantized compression (even for very finely quantized, high qualitycompression), and in comparison to the noise floor in the originalimage. This aspect of the invention can be extended to 16-bit OpenExrfloating point, to 24-bit floating point, to 48-bit, 64-bit, 128-bit,and all other useful floating point computational formats. With theexception of 16-bit OpenExr “half” floating point, all other commonfloating point formats have more than 16-bits, and provide higherprecision than is available with 8, 12, or 16-bit integer computations.Further, the increased precision of floating point eliminates the needfor an “exact match” between the integer computations in the decodersubsystem within the encoder, and the integer computations of the finaldecoder, in order to eliminate pixel error drift and accumulation duringmotion-compensated decoding. This is because the increased precisionminimizes drift in pixel values which can commonly occur in theleast-significant-bit roundoff of non-exactly-matching computationalorders. With ubiquitous 32-bit floating point processing within an imagecompression system, thousands of computational steps can be applied topixels between intra frames without any significant error accumulatingin the least significant bit of a 16-bit pixel.

All known high-compression-ratio coding systems, including the presentinvention, require an integer as the result of the quantization stepwhich is applied after the transform. However, in the present invention,the transform value is 32-bit floating point (or other-sized floatingpoint), and the dividing quantization parameter is 32-bit floating point(or other-sized floating point). Only the divided result is truncated toan integer. Each resulting integer is subsequently coded using alossless variable-length coding system, and then reconstructed bymultiplying the floating-point quantization value (which is typicallyconveyed in the coded bitstream or coded bit files). Note that it iscommon practice, including with the present invention, to allowdifferent quantization values for different color channels, such as R,G, and B, or Y, U, and V.

Another aspect of the present invention applies an adjustablefloating-point deadband, via subtraction and zero-floor during encoding,and via addition during decoding. For example, integer (max((Value−D),0)/QP), where D=an adjustable floating point parameter, and the sign ofthe Value is conveyed separately in the output bitstream. Further, thedeadband can be adjusted regionally, and/or as a function of whichlayer, to best optimize the compression coding. In this way, criticalsmall integer values can be made most accurate using small or zerodeadbands, and larger integer values, or less critical small integervalues, can generate less coded bits via use of a larger deadband duringquantization.

Extended Range

Typical compression systems such as JPEG-2000 and MPEG-2 utilizefixed-point pixel data, with the maximum pixel brightness correspondingto the maximum integer code value, or near that value. For example,white for luminance (or red, green, and blue) in 8-bit coding is usuallyeither 255 or 235 (if it is 235, values between 236 and 255 areundefined). For 10-bit coding, white is usually 1023 or 940 (if it is940, values between 941 and 1023 are undefined). Similarly, black for8-bit coding is usually either 0 or 16 (with values below 16 undefined),and for 10-bit coding is usually either 0 or 64 (with values below 64undefined).

With OpenExr 16-bit floating point, and with 32-bit and other floatingpoint, there is no need to restrict pixel values to the logical range0.0 to 1.0. OpenExr 16-bit floating point extends to 16384 times areference white value at 1.0, and can represent very small values.Further, negative values can be represented.

With the coding system of the present invention, the inherent wide rangeof floating point representation is preserved throughout compression.This includes values above 1.0, very small values, and negative values.This can be very useful when compressing a wide-range source masterimage, since the final viewed white may be clipped to a value far belowthe bright values inherent in the original master. Digitally scannedfilm negatives, and wide-range cameras like the Thomson Viper inFilmstream Quasi-logarithmic mode, typically capture a much wider rangeof bright and dark values than are presented in the finished displayimage. The compression technology of the present invention allowsefficient high quality coding of wide-range image data as well asfixed-range distribution coding.

In a purely logarithmic representation of image data, brightnesses canbe scaled and color-balanced simply through addition and subtractionoperations. In a linear floating-point representation, brightnesses andcolor balance are achieved by scaling multiplication. The compressiontechnology described in the present invention can be applied topre-balanced (i.e., unbalanced) image data, to rough-balanced data, orto final-balanced data. Compression is efficient and preserves the fullimage quality over the entire range independent of such balancing, andindependent of whether the whites are clipped to a logical value of 1.0.Further, the use of floating point representation in the quantizationparameter allows a wide dark range as well as a wide bright range.Further, negative pixel values can be directly coded (which can beuseful in intermediate image subtractions or differences, or othercreated intermediate image elements).

Since the extended range representation of values is preservedthroughout the compression process, the present invention can compressimage information other than brightness, such as depth maps, infra-redimages, texture height fields, radar reflectivity, and other suchrelated useful information. The data range, noise floor, and otherrelevant information about such data can be taken into account tooptimize the parameters of compression encoding. Even without suchinformation, the present invention can directly code all of these formsof image-related non-brightness data.

However, one of the most significant practical benefits of this aspectof the invention is the ability to efficiently compress wide-rangemoving and still image data, beyond the brightness value implicit in theselected numeric representation of the final displayed reference white.

More Optimal Band-Split Transform Coding

Many aspects of the present invention can be applied to the DWT 9/7bi-orthogonal subband wavelet transform. However, the present inventionis not limited to that particular band-split method. In fact, the DWT9/7 bi-orthogonal subband wavelet transform has a number of flaws whenviewed as a band-split filtering method. These limitations can besummarized as follows:

(1) The low-pass and high-pass filters of the DWT 9/7 transform usefilter kernels (FIR kernels) which vary somewhat from more optimalband-split filters, such as the windowed-sinc function described above.The net result of the variation from optimal is the introduction ofaliasing. In compression, aliasing both reduces quality as well asincreases the number of compressed bits required at that reducedquality.

(2) The DWT 9/7 bi-orthogonal transform is constructed by using amutually orthogonal hi-pass and low-pass filter pair, centered atalternate pixels (even and odd, or odd and even). The kernels arereversed, such that the low pass filter kernel during the originaltransform becomes the high pass filter during the reconstruction, andvice versa. This structure allows a sub-band decomposition wherein thereresults a low-low (horizontal and vertical), low-high, high-low, andhigh-high band. The benefit of this approach is that the low-low band isfurther decomposed for lower layers. The drawbacks of this structure areas follows:

(a) The low-low band hierarchy is centered over a single pixel, with allhigh bands adding adjacent horizontal and vertical pixels. There is thusa positional shift, by a fraction of a pixel, at each layer.

(b) This structure does not optimally distribute the transformed energy,due to this pixel-centered low-low phase construction.

(c) The sub-band hierarchy is limited to factors of two in resolutiondecomposition.

(3) In typical application, such as in JPEG-2000, the DWT 9/7 is appliedto non-linear pixel values. This is an additional deviation from optimalband-split filters based upon windowed-sinc filtering of linear pixelvalues. The result of this deviation is that sharp edges have additionalaliasing, degrading quality and require additional bits at that degradedquality.

Down-Filtering

One aspect of the present invention is to use a filter phaseconstruction in which low-pass bands are created in between pixels byappropriate selection of filter tap weights. This is in contrast to theDWT 9/7 filter structure which is exactly coincident with half of thepixels.

FIG. 2 is a block diagram showing application of an optimaltwo-dimensional spatial band-split filter in accordance with this aspectof the invention, using a factor of two in this example. In order toproduce a high-frequency difference image 200, an input original image202 (a full-resolution image in this example) is down-filtered using alow-pass kernel 204 to a half-resolution image 206. The low-pass kernel204 implements a windowed sine function with n-taps and applied to aphase half-way between original image pixels in this example. Thewindowed sinc function has first zero crossings spaced at 2.0 pixelunits (plus and minus one pixel) of the half-resolution image 206.

The half-resolution image 206 thus contains the low-band spatialfrequencies of the original image 202. An up-filter 208 is applied tothe half-resolution image 206 using a windowed sinc function with m-tapsto generate a reconstructed image. The windowed sinc function has firstzero crossings spaced at 2.0 pixel units (plus and minus one pixel) ofthe half-resolution image 206. The phase of the reconstructed image usespixels that are only a quarter-pixel distant. The reconstructed image isthen subtracted 212 from the original image 202 to produce the desiredhigh-frequency difference image 200.

Note that the process shown in FIG. 2 also can be applied to a reducedresolution image of a higher layer.

FIG. 3 is a diagram of a row of original pixels 300 showing thecoincident (with respect to the pixels 300) phase construction for theDWT 9/7 structure. A low-pass filter function 302 is applied to theoriginal pixels 300 such that the center phase (i.e., at 0.0 in thescale 303) of the low-pass filter function 302 is exactly centered onevery other (even or odd) pixel. The result is the generation of ahalf-resolution pixel set 304 (note that the depiction of pixel sets 300and 304 are magnified; the actual scale of such pixels 304 is shown at303). However, by centering the phase of the low-pass filter function302 over an original pixel, the generated pixels 304 primarily compriseenergy from the centered original pixel. During the inverse transform,reconstructed pixels are a half-pixel distant during the up-filteringstep.

FIG. 4 is a diagram of a row of original pixels 400 showing anintermediate (with respect to the pixels 400) phase construction for thepresent invention. A low-pass filter function 402 is applied to theoriginal pixels 400 such that the center phase (i.e., at 0.0 in thescale 403) of the low-pass filter function 402 is positioned in betweenthe original pixels 400. The result is the generation of ahalf-resolution pixel set 404 (note that the depiction of pixel sets 400and 404 are magnified; the actual scale of such pixels 404 is shown at403). The use of an intermediate phase low-pass filter construction inthe present invention is more optimal than the DWT 9/7 structure. Thisis because reconstructed pixels, during the inverse transform, are onlya quarter-pixel distant during the up-filtering step. Thus, whenapplying the present invention, less energy is used for thequarter-pixel distant transform phase versus the DWT 9/7 bi-orthogonalsub-band phase.

The more optimal band-split filter phase structure, shown in FIG. 4using a factor of two in resolution, as with the DWT 9/7. However, thegeneral concept of this aspect of the present invention is that ofcentering the phase of down-filtered pixels between source pixels, asopposed to placing some or all of the down-filtered pixels in the sameposition as source pixels (as with DWT 9/7 bi-orthogonal subbandwavelets). Since an optimal band-split filter phase structure supportsarbitrary resolution relationship fractions, the phase may be inherentlyvarying, as implied by the specific fraction. Thus, a ⅔ resolutiondown-filter has two down-filtered pixel phases for each threefull-resolution pixels, whereas a ⅓ resolution down-filter naturallycenters the down-filtered pixels over the center of the second of everythird full-resolution pixel. The phase difference is such thatsuccessive down filterings in the style of the DWT 9/7 subband wavelettransform will be centered over the first (or second) pixel, thusresulting in a net displacement by a half of a pixel with eachdown-filter step. In contrast, the optimal band-split filter phaseaspect of the present invention does not result in any fractional pixeldisplacement with each down filter step. Another way to view this isthat a down-filter by a fraction of ¾ centers new 3 pixels (numerator)over the span of the original 4 pixels (denominator).

Arbitrary ratio relationships can be easily implemented by using apoly-phase truncated sinc filter, typically with several dozen phases,where the sinc width between zero crossings is set to two pixels wide atthe lower of the input and output resolution. For up-filtering, thelower resolution is the input resolution. For down-filtering, the lowerresolution is the output resolution. Both filters are applied in atypical application (going from a given level, or the original level, toa lower resolution level), since the image resolution is firstdown-filtered, quantized, and then up-filtered to subtract from theoriginal (or higher level) resolution to create the difference values toquantize for the next level up.

The disadvantage of the more optimal band-split filter phase of thepresent invention when down-filtering is that an up-filter step adds adelta for every pixel in the final up-filtered image. Thus, five sourcepixels (original down-filtered, plus four deltas) are required for fourresulting up-filtered pixels. The DWT 9/7 subband wavelet, however, needonly add the high bands, resulting in four source pixels (low-low,hi-low, low-hi, and hi-hi) for every resulting four up-filtered pixels.Thus, there is 5/4 more data (in the factor-of-two resolution case) inthe more optimal band-split filter phase structure, versus the DWT 9/7subband wavelet structure. However, the amount of data at the up-filterstep is not the primary concern in digital image compression. Theprimary concern is the amount of data after quantization andvariable-length-coding. Given that the more optimal band-split filterphase structure of the present invention have smaller deltas, by perhapsas much as a factor of two, the more optimal band-split filter phasestructure often outperforms the DWT 9/7 for image compression efficiency(i.e., ratio of output bits to input bits).

Note that which structure ultimately is better is scene dependent. Itmay depend on the type of image, the amount of noise (or grain) in theimage, and frame-to-frame motion (if there is motion compensation).Further, the specific resolution level also affects which is better,since mid levels of image resolution can best benefit from the DWT 9/7structure, and the aliasing damage from suboptimal filters in the DWT9/7 is least problematic in these mid levels. Lower levels and higherlevels, however, both usually are more optimal with the more optimalband-split filter phase structure of the present invention.

Given that the implementation of both the DWT 9/7 subband wavelet andthe more optimal band-split down-filter and up-filter structure isrelatively straightforward, both are easily implemented. In this wayboth filter structures can be tested and coded at each level, on eachframe, to determine which is more efficient (i.e., outputs the leastbits). If both transforms use the same quantization method, the qualityshould be relatively equivalent, so the determination can be made solelyon the number of bits generated. Further, this determination can beapplied regionally within an image, although boundary conditions makethis somewhat complex. Even being applied on a per-level basis, theability to dynamically determine whether to use the DWT 9/7 or the moreoptimal band-split filter (based upon truncated sinc) structure canresult in substantial coding efficiency gain.

SNR Improvement

The benefits of the more optimal band-split filter structure also applyto SNR improvement layers, at any resolution level. The highestresolution levels are likely to favor the more optimal band-splitfilter, since the reduced size of the deltas from the more optimalband-split filter will usually be more beneficial than the DWT 9/7subband structure. However, the selection of whether to use the DWT 9/7subband wavelet transform of the more optimal band-split filtertransform at each SNR layer can be dynamically determined by the numberof bits generated (if using the same quantization for each during thecomparison).

The DWT 9/7 subband structure allows optional SNR improvement on thelow-low-band if that band is present. The optional low-low-band canimprove the SNR prior to adding the high-low, low-high, and high-highbands. During decoding, the decoding of the low-low SNR improvement bandcan be optional, as determined at the time of decoding. It is alsooptional as to whether a low-low SNR improvement band is created duringDWT 9/7 subband wavelet SNR processing. The equivalent structure usingthe more optimal band-split filter structure of the present invention isto apply an optional SNR band at the lower resolution prior toup-filtering. Such SNR bands (using the more optimal filter) are createdat the same resolution as the image that they are improving. Thus, boththe DWT 9/7 subband wavelet and the more optimal band-split filtersupport low-low resolution level SNR. However, with the more optimalband-split filter, the low-low resolution SNR should preferably beapplied prior to up-filtering, with additional SNR layers applied at theup-filtered resolution, whereas the DWT 9/7 subband can be optionallyapplied at the time of up-filtering. However any further SNR layers atthe higher resolution should include the DWT 9/7 low-low subband, if itis created during up-filtering.

Note that SNR improvement layers can be applied at any resolution level.Further, when using the more optimal band-split filter structure, theresolution steps need not be limited to a two-to-one ratio (whereas theDWT 9/7 subband wavelet is strictly limited to a two-to-one ratio, or anSNR layer at a one-to-one resolution ratio).

FIG. 5 is a block diagram showing one implementation of an imagecompression system having a resolution and SNR layered structure, withoptional SNR layers at various resolutions. A source image 500 isband-split in accordance with the teachings of this invention into oneor more lower resolution images 502, 504, 506 (other number of lowerresolution images may be used). The lowest resolution image 506 is leftunquantized and represented in floating point format 508.

Moving up the chain of bands, the next lowest band-split image 504 isquantized and then dequantized, and then combined with an up-filteredversion of the unquantized lower resolution image 506 to create adecoded image 510. Continuing up the chain of bands, the next lowestband-split image 502 is quantized and then dequantized, and thencombined with an up-filtered version of the decoded image 510 from thenext lowest level to create a high-band coded image 512. This high-bandcoded image 512 may be subtracted 514 from the corresponding band-splitimage 502 to generate an SNR difference image 516, which may be outputto a decoder and optionally used within the encoder for further imageprocessing.

The high-band coded image 512 may also be up-filtered and combined witha quantized/dequantized version of the source image 500 to create a fullresolution coded image 518. The full resolution coded image 518 may thenbe subtracted 520 from the corresponding source image 500 to generate afull-resolution SNR difference image 522, which may be quantized,dequantized, and then output to a decoder.

FIG. 6 is a block diagram showing one example of an image compressionsystem having layered compression processing. A source image 600 isband-split into at least high band 602 and low band 604 lower resolutionimages. In this example, if motion compensation is used, a decodedprevious low resolution frame 606 is subtracted from the low band image604 to generate an intermediate frame 608. As in FIG. 5, theintermediate frame 608 can be band-split into one or more lowerresolution images 610, 612, which are combined into increasingly higherresolution coded images 616, 618 (image 614 being an unquantized,floating point version of image 612). The highest level coded image 618is then added to the decoded previous low resolution frame 606 togenerate a real (viewable) image 620.

This image 620 can be combined with a quantized/dequantized version ofthe high band image 602 to generate a full resolution coded image 622.Targeted aspects of the full resolution coded image 622 can be comparedwith source image 600 to generate a full resolution first SNR image 624.If desired, further aspects of the difference between the source image600 and the first SNR image 624 can be targeted to generate a fullresolution second SNR image 626. Similar SNR processing can continue ifdesired.

Up Filtering

The DWT 9/7 subband wavelet uses a low synthesis filter kernel of7-taps. Thus, for each low band transformed pixel, 7 pixels in the nexthigher band are affected during synthesis to the next higher resolutionlevel. The high bands use a synthesis filter kernel of 9 taps, such thatfor each high band transformed pixel, 9 pixels are affected duringsynthesis.

The more optimal band-split filter is similar at low resolution bands,in that the windowed sinc kernel to up-filter the low-band has a similar“extent” (i.e., the degree to which a low band transformed pixel affectsone or more pixels in the next higher band). However, the high banddifference image is not filtered, but is rather added directly to theup-filtered low-band. Thus, the high band difference pixels, at a givenlevel, do not contain any extent beyond their single pixel. However, atthe next resolution band up, the pixels become the next low band, andthe windowed sinc kernel extends their influence according to its size.The final high resolution level, or any lower resolution level which isdisplayed or saved, has the high band pixels with a single-pixel extent.Thus, the high-band difference pixels in the more optimal band-splitfilter are single-pixel differences, with each adjusting a single pixel,and not influencing adjacent pixels at its level. Because of this, thereis not strict equivalence between the high bands of the more optimalband-split filter transform and high bands of the DWT 9/7 bi-orthogonalsubband coding. Even if the same quantization value is used for the highbands of the more optimal band-split filter structure and the DWT 9/7transform, the appearance is different because of the 9-pixel high-bandfilter extent of the DWT 9/7 transform versus the single-pixel high-bandextent of the more optimal band-split filter structure. Because of this,the choice of whether to use the DWT 9/7 transform, or the more optimalband-split filters, should take into consideration the difference inappearance of these transforms. Even if both types of transform arequantized the same, the appearance is different. Thus, the transformwith the lowest number of bits at a given quantization is not strictlythe only criterion for selecting between the transforms.

Note that high-band pixels from the more optimal filter are appropriatefor resolution-increasing layers, as well as for SNR layers at anylevel, including SNR layers at the highest resolution level.

Pixel Extent & Image Degradation

The property of pixel transform coefficient influence or “extent” isshared among DCT and other transform codes. For example, each of the 8×8DCT coefficients influences an 8-wide by 8-tall pixel region wheninverted. Similarly, a single pixel value in the original 8×8 pixelblock affects all of the 8×8 coefficients in the transformedcoefficients of the DCT. The main difference between the DCT and thefilters described here, both more optimal band-split and the DWT 9/7bi-orthogonal subband, is that the region of influence of the latter isapproximately centered (plus and minus one pixel) about the currentcoefficient, whereas the DCT coefficients are associated only with afixed-position block. Thus, a pixel at the edge of an 8×8 DCT block doesnot affect the adjacent block. A quantized DCT can thus exhibitblock-edge artifacts, whereas the more optimal transform coding and DWT9/7 bi-orthogonal subband do not degrade in this way. Instead, thedegradation of quantized DWT 9/7 low and high bands appears as anunnatural “footprint” of the low (7-tap) and high (9-tap) synthesiskernels, as concatenated over all levels. In contrast, while the moreoptimal band-split filter may have an unnatural appearance due to thelow-band up-filter kernel, concatenated over all levels, it only haslocal pixel inaccuracies, limited to single pixels, at the highestviewed level. Note that the more optimal band-split filter's low-bandup-filter windowed sinc filter is often relatively invisible, since itpreserves the soft low-band image appearance, without adding unduekernel ringing (if truncated at a relative small size, such as ±3 pixelsor less). Thus, the more optimal band-split filter exhibits softness dueto quantization, as well as overall pixel errors due to quantization,but does not exhibit as much ringing or other kernel footprintappearance artifacts as the DWT 9/7 transform.

This feature of the more optimal band-split filter allows a moregraceful degradation when bits are reduced (by coarser quantization) inhigher spatial bands. The image becomes softer, but does not exhibitaliasing. In contrast, the DWT 9/7 bi-orthogonal subband waveletexhibits aliasing (as well as softening) if the high band bits arereduced (by coarser quantization). In general, softening alone ispreferable to both softening and aliasing. Note that thisquantization-caused aliasing is different from the DWT 9/7 aliasinginherent in lower bands, which requires more bits to code, but which isremoved as higher bands are resynthesized.

Alternative Embodiments

It should also be noted that a less optimal phase structure centeredover a pixel can be used with filters more similar to a truncated (alsocalled “windowed”) sinc than the DWT 9/7 subband wavelet filter kernel.The DWT 9/7 wavelet kernel is bi-orthogonal, which restricts the filterkernels to specific bi-orthogonal wavelet pair sets. When using a moreoptimal band-split filter which is more similar to a truncated sinc, thesub-band structure with low-low, high-low, low-high, and high-highcannot be used. However, the less optimal filter phase with a moreoptimal filter kernel, providing a better band split than the DWT 9/7bi-orthogonal subband kernel, also offers benefits in some applications.However, the more optimal band-split filter phase structure is usuallypreferable.

Note that the use of a ⅔ resolution hierarchy forms a better match withthe HDTV and SDTV formats in the United States of America, since ⅔ of1920×1080 is 1280×720, and ⅔ of 720 is 480 (Standard definition). Themore optimal band-split filter can also directly handle the oddrelationships between 1280 to 640, 704, or 720, which are the typicalhorizontal values associated with 480 vertical Standard Definition.Further, the relationship of 1920 to 640, 704, and 720 can also bedirectly handled.

Digital Cinema is exploring horizontal values of 4096, 3840, 3656, 3072,2560, and 2048, in addition to the 1920 used by HDTV. Similar sets ofvalues are being explored for vertical resolution, corresponding to the1.85:1 and 2.37:1 aspect ratios typical for movies. The flexibility ofthe more optimal band-split filters structure of the present inventionprovides direct support for all of these odd horizontal sizerelationships.

Targeted SNR Layers

A common practice is to have a single SNR layer in compression systems,at the final resolution. However, in another aspect, the presentinvention uniquely uses entire SNR layers at lower resolution levels.Further, this aspect of the invention extends the notion of having theSNR resolution layer at the final resolution by allowing multiple SNRconstructions. The use of the optional low-low band in a DWT 9/7bi-orthogonal sub-band wavelet for the SNR layer is one such novel SNRstructure. The use of optional switching between the more optimalband-split filter and the DWT 9/7 bi-orthogonal wavelet, based uponleast bits with a common quantization, is another novel aspect of thisinvention.

This aspect of the invention further extends these concepts to havingmultiple SNR layers at the full resolution level or at any lowerresolution level.

In addition, another unique aspect of this invention is the targeting ofspecific SNR layers to specific quality improvements. For example, oneSNR layer can be applied to details in the image, using a high-pass orband-pass filter to select specific spatial frequencies for that SNRlayer. For instance, a band-pass filter between ⅓ of full resolution and¾ of full resolution can be used to weight pixel differences whichimprove this selected resolution band. Further, multiple pass-bands canbe summed, such that a particular SNR layer can improve several bandssimultaneously. In contrast, the common practice of SNR improvement doesnot target specific frequencies (i.e., it is an all pass filter).

For another example, one SNR layer can be targeted to improving thequality of mid-gray portions of the image. Thus, a filter would beapplied to the SNR layer, proportionally selecting pixels which aremid-gray, based upon how close they are to mid gray (both in color andbrightness). The resulting pixel differences (as weighted by theirproportion of being mid-gray) can then be coded as a normal SNR layer atthat resolution level (including full resolution). Further, specificcolors and/or brightnesses can be selected instead of mid-gray. Forexample, dark regions and bright red regions could be selected usingstandard color selection or lookup methods, thereby forming a proportionused to weight SNR pixel differences for an SNR layer at any resolutionlevel. A simple way to target dark regions of the image is to build aproportion based upon the inverse of the sum of the pixel values asdecoded at any level. Such an inverse is largest when the sum of thepixel color components is low (e.g., where red+green+blue is small orluminance is small). This proportion thus forms a weight whichemphasizes dark pixel differences in an SNR layer designed to improvedark detail. Similarly, a color extraction where red minus green plusred minus blue is used naturally emphasizes reddish-color pixels.Another useful targeting method would be to use YUV or other colorspaces for some layers, and RGB for other layers. It may also be usefulto use just luminance (Y). Many other natural color and brightnessselection techniques can be used in order to target SNR layers. Incontrast, the common practice of SNR improvement does not targetbrightnesses or colors (i.e., it passes all colors and brightnesses).

Thus, targeting of specific features can be specified for each SNRlayer. In general, the decoder need not know those specific featuresused in SNR layers, such as band-pass and color-selection weights, whensuch weights merely scale the difference values during encoding toselect the targeted pixels of interest. Those difference values aredecoded without further scaling, and are thus inherently applied to thetarget improvement. However, some forms of weighting, such as aweighting of pixel differences based upon a dark weighting, may be mosteffective if the identical weighting formula is applied during decodingof that SNR layer, based upon decoded pixel values (the same decodedvalues used during encoding). Thus, it may be necessary for some formsof targeted SNR to signal to the decoder which SNR-type is being appliedat each SNR layer, so that the decoder can reproduce the appropriateweightings. In general, SNR-specific decoding features affectnon-uniform weightings of quantization, either via weighting thedifference signal in such a way as to require an inverse-weightingduring dequantization, or alternatively via weighting the differenceamplitudes, which is unweighted during decoding.

Note that some target weighting methods involve per-pixel targeting,such as the inverse of the sum of decoded red, green, and blue to targetdark pixels. Such weighting methods require a similar process in thedecoder for each pixel. Other weighting methods, such as weightings tothe SNR difference values, may be applied in the encoder with adifferent weighting at every pixel, but need have no inverse weightingprocess applied during decoding, since the encoded weights are intendedto be left “baked in” during decoding as if they were inherent to theoriginal image signal. Still other methods, such as wide-range scalingof pixel differences, or use of regional adjustments to the quantizationvalue, require regional processing in the decoder to properly re-adjustthe scale of the differences, or to regionally adjust the quantizationvalue for decoding so that it matches the quantization value used duringencoding. Which method or methods are used can be signaled to thedecoder, and each method is useful for all portions of thetransform-coded values, including SNR, the motion compensated differenceimage at any layer, and resolution enhancing-layer encoding of intraimages or motion-compensated difference images. It is also useful tomix-and-match all of these targeting methods in each frame, to optimizethe application of these methods. However, the main intent of thesemethods is to apply one or more method to each SNR layer to targetspecific image features for SNR improvement.

For those SNR layers not requiring special decoding methods, thetargeted pixel differences preferably are added with each layer, usingthe normal dequantization and filter reconstruction (which is synthesisin the case of DWT 9/7). Since such differences are known to theencoder, the encoder's decoder applies these differences, and is thenready to take new differences from the original (at that resolutionlayer, possibly full resolution). The new differences can then beweighted with more of the same target, or with different targetfeatures, or combinations of these, and so-on, for as many targeted SNRlayers as are created during encoding. Thus, targeting of one or morefeatures within each SNR layer is easy to apply iteratively for multipleSNR layers, without any relationship being required between the types oftargeting.

Of course, the decoder should know (by signaling or implicitly) whichSNR layers to apply in which order, since further SNR layers applyadditional SNR improvements to the concatenation of previous layers.However, it may sometimes be beneficial to decode somespecifically-targeted SNR layers without decoding previous layers, sincethe previous layers may not have targeted features which may be mostneeded during that specific decoding. However, selective decoding of SNRlayers will usually be applied by selecting in-order, such that each SNRlayer improves the concatenation of all previous, and eliminated SNRlayers would all be logically subsequent to the stopping point (the lastSNR layer applied).

Note that full-resolution SNR layers generally will be consideredoptional to the decoder, since each full-resolution SNR layer's functionis to provide improved picture fidelity with respect to the originalimage.

Further, SNR layers at lower resolution levels is also optional for thatlevel, although they usually, but optionally, are required prior toapplying high resolution layers and levels.

Spatial Targeting

Efficiency can be gained in some applications by concentrating codingbits in regions of interest. Another way to view this is that bits usedto create details and nuances in regions of little interest are beingwasted. For example, many television receivers use “overscan” whichplaces the edge of an image outside of the viewable display area, makingthose outside portions invisible. Overscan in the range of 4% to 8% istypical. However, some receivers, and computer displays showingdecompressed images, do not overscan. It is thus useful to create thefull image, but to concentrate coding bits on regions away from theframe edges.

This is easily accomplished in targeted band-split and SNR layers bydecreasing the amplitude of pixel differences or high bands near theedges using a weighting which decreases, possibly to zero, as a functionof nearness to the frame edge. In the high-frequency bands, the decreaseresults in decreased sharpness and detail. In SNR bands, the decreasewill result in less accuracy, clarity, and even less noise, at the frameedges.

It is also possible to discover which regions of the image are in-focusversus which regions are out-of-focus. This can be accomplished usingany useful form of variance measure at any band at or above themotion-compensated layer (or at any useful mid-detail layer forintra-coding). For example, at a suitable mid-detail spatial frequency,such as ¼ of the full resolution, the variance from average ofluminance, or any useful function of red, green, and blue (such as redplus green plus blue plus hue plus saturation) of the decoded image atthat layer can be used to determine local regional detail. Suchinformation can then be used to weight higher resolution high-passlayers, as well as SNR layers at that or higher resolution layers. Ifthe variance from average is small, low-amplitude details may still bepresent, suggesting that the amount of weighting should not modulate allthe way to zero, but rather to some useful fraction of full weight.

Note that such weightings need not be conveyed to the decoder, since theintent is to reduce the amplitude in certain regions (such as near theedge), and that reduced amplitude is already present after theweighting. Thus, the reduced amplitude during encoding is automaticallyreproduced during decoding.

If regional quantization values are conveyed to the decoder, some typesof weightings can be applied to the quantization, or equivalently to aregional scaling of the pixel differences (which would also need to beconveyed to the decoder). The quantization deadband can also be widened,in addition to coarser quantization, in regions of lower interest,although the regional deadband adjustments are also conveyed to thedecoder.

Thus, there is a natural choice for regions of lower interest betweencoarser quantization versus reduced amplitude, versus a combination ofboth coarser quantization and reduced amplitude. Information is conveyedto the decoder when using regional quantization adjustments, and/orregional deadband, and/or regional amplitude scaling and re-scaling. Noadditional information need by conveyed by regional weightings which usethe quantization and deadband which would have been present if noweighting was applied, and thus where such weightings are intended to bereproduced during decoding as if they were inherent to, or “baked in”,to the original image signal.

Levels of resolution in a band-split resolution hierarchy are separatedinto a low-band and one or more high bands. The low bands are the actualimage, at or above the motion compensation layer. The high bands containdetail to be added to the image to increase resolution, or to improveSNR. Thus, variances in the low bands represent picture detail at midand lower frequencies. The high bands are themselves variances, to beapplied about the low-band pixel values as their local averages. Thus,low-bands and high-bands are used differently when determining regionalimage detail at various resolution levels.

In addition to edge-nearness and mid-band variance, other useful methodscan target regions for lower weightings and/or coarser quantization. Forexample, a person can circle regions of high interest, or alternatively(and equivalently) regions of low interest, and can also adjust theamount of weighting and/or of quantization coarseness to be applied.Other algorithmic fully automatic methods, other than variance, can alsobe applied to one or more bands at or above the motion-compensatedlevel. For example, a trend of high frequency amplitudes in theband-split hierarchy indicates whether high frequencies are decreasinggreatly, decreasing slightly, or not decreasing at levels of increasingspatial resolution. Such a trend usually is indicative of the imagedetail to be found in higher layers, versus the high frequency noisethat is likely to have increasing amplitude at the highest levels(without containing much useful visual detail). Another method (withfuture cameras creating such metadata) is to use a depth map of theimage, combined with information about the lens focal distance, todetermine which regions of the image are in focus. Other analyticmethods for determining regions of the image which are of interest, andwhich are in focus, can be utilized to weight the high frequency bands,the SNR difference values, and/or the quantization coarseness/fineness.

Static Enhancement Targets

It is sometimes useful to establish a collection of statically-assignedtargets for high-pass layers and/or for SNR layers. For example, layersassigned to enhance the left, center, and right sides of the screen canbe always statically targeted. In this way, during decoding, whicheverregions are of greatest interest can be decoded, while those of lowerinterest need not be decoded. In this aspect of the invention, themethods described below allow low overhead for each layer, beyond thedata required for the tasks assigned to that layer. Thus, numerousregional enhancement layers (both high-band and/or SNR) can be provided,allowing detailed decoder focusing on specific regional areas. Forexample, several dozen regions can be enhanced, but perhaps only ahalf-dozen decoded for those regions of specific interest for a givenapplication.

Similarly, an SNR layer assigned to dark regions and shadows is oftenuseful, such that an interest in the shadow detail can always beoptionally decoded by decoding that specific SNR layer. The sameapproach can be used for SNR layers assigned to bright areas, saturatedcolors, specific sets of colors, or combinations of these. Similarly,edge-assigned SNR and high-band layers can be utilized formulti-aspect-ratio decoding, such that each screen shape (aspect ratio)can decode those layers appropriate for that shape. The center region ofan image, which is common to all intended decoded aspect ratios, canalso have various improvement layers.

Combinations of all of these above targets can also be provided in eachlayer. For example, a layer can target several disparate regions, inaddition to dark shadow areas at all points in the picture, in additionto saturated colors in other regions. FIG. 7 is a diagram of a sourceimage 700 showing a scene suitable for generating targeted layers. Atree in the source image 700 has a generally green area 702 and brownarea 704 as well as a dark shadow area 706 that can be independentlytargeted for coding using one or more SNR layers. Since some portions ofan image may be out of focus (unsharp) 708, those portions of an imagethat are in focus (sharp) can be separately targeted for coding usingone or more SNR layers. Thus, targeting allows wide flexibility in bothencoding and decoding structures to optimize the usefulness of theinformation provided in the encoded bitstream (or bit files).

SNR Difference-Controlled Quantization

It is common practice to apply a single quantization value to an SNRlayer. Larger coded differences (compression coding errors) generatemore coded bits than smaller coded errors, in a relatively uniformmanner. In those cases where quantization varies by region (usually on“macroblock” boundaries), the common practice is adjustment according tovariable rate control to meet a specific target for the number of bitsbeing generated (usually to meet a realtime buffer and delayconstraint). Such methods for applying quantization to SNR improvementlayers provide limited control over the location and amount of theimprovement. Further, there is an assumption that the amount ofdifference is relatively uniform over the image, or only varies if thequantization is varying for the underlying coding (usually viarate-control for the layers below the SNR layer). However, some codingdifference errors are due to concatenated precision errors resultingfrom quantization during computation (not quantization for compression).In a deeply-layered transform coding system, such as DWT 9/7 subbandcoding, or deeply-stacked band-split transform coding, concatenatederrors can accumulate using floating-point or fixed-point transformprocessing due to the large number of computational steps. Suchcomputational difference errors add to quantization difference errors atevery layer, resulting in a statistical variation of the error, withoccasional large errors at several times the standard deviation(“sigma”). In order to reduce such maximum worst-case difference codingerrors, improved precision, via decreased quantization, can be appliedto the regions containing such errors.

Common practice SNR in image compression uses a uniform quantization.However, in another aspect, the present invention improves the SNR byusing a lower quantization value (better precision) for regions of theimage showing a higher difference (compression coding error) from theoriginal (for that resolution layer). Typically, the amount ofdifference varies with each pixel. In this aspect of the invention, theaverage difference over a region, or the maximum difference over aregion, are utilized to control the number of bits generated in thatregion, directly or indirectly via regional adjustment of the precisionof quantization. Combinations of the average and maximum difference canalso be used, as well as monotonic functions of these methods. Thegreater the average, maximum, or other monotonic function of theregional difference, the lower the quantization value should be to applythe greatest correction to that region. In this way, an SNR layer canoptionally be weighted to apply most of its bits to the regions ofhighest compression coding error.

Regional determination of the difference can optionally includefilter-kernel-sized edge overlap when analyzing a region, to ensure thatall pixels affecting the coding of the region are used to determine thequantization value (even at those pixels beyond the edges of the region,but which affect the region due to filter-kernel-size).

One method of determining the region, in accordance with this aspect ofthe invention, is to band-split the difference image using DWT 9/7bi-orthogonal subbands or other band-split filters in a layeredhierarchy. After a selectable number of levels, a low-resolution imageis created which represents the average of the difference over thatregion (including edge overlap).

As an alternative or in addition, the maximum difference (compressioncoding error) of each region can be applied to a resolution hierarchy(including edge overlap) to determine a low resolution quantizationvalue for each region. This maximum can also be implemented as a minimumof the inverse of the compression coding error, or by creating aninverse as a function of the average difference and/or maximumdifference. This approach allows precise targeting of SNR improvementsto specific regions of the image as a function of the difference (codingerror) in each region.

A function of the maximum difference, average difference, or a combinedfunction of both, can be utilized to weight the quantization of SNRdifference pixels. Alternatively, and equivalently, the pixel differencevalues themselves can be regionally weighted. A combination of bothregional quantization weighting and pixel weighting can also be used. Ineither case (quantization and/or pixel weighting), the function ofmaximum and/or average difference can be set to give a controlled amountof correction to the largest error regions. Usually this is a largeramount of correction. However, it is sometimes useful to apply a uniformamount of correction (not a function of the size of the average and/ormaximum difference) which can then be used with a non-linearrepresentation of pixel brightness. There may even be cases where areduced amount of correction is useful in regions of largest average ormaximum error, such that quantization favors more correct regions. Morebits often are still applied to the regions of largest difference sincethose regions continue to generate more bits, even with a higherquantization than other regions (the larger values, even when scaled ordivided by the higher quantization, may still generate substantialamounts of bits).

It is common practice to compress a non-linear representation of thelight value represented by each pixel. The non-linear representation isusually a gamma-adjusted video signal (with approximately 2.2 gammabeing typical). If an SNR difference (coding error) is determined bysubtracting the non-linear pixel values from the coded values, aninherent perceptual weighting is implied. Thus, a difference of onepixel unit (one lsb in 8 bits or 10 bits, for example) represents asmaller difference in light value for a dark pixel than for a brightpixel, due to the non-linear representation.

In a YUV representation (also called YPbPr and YCbCr), the U (Pr Cr) andV (PbCb) channels are R-Y and B-Y, respectively. These R and B valuesare non-linear due to gamma adjustment. However, the R-Y and B-Y valuesare not perceptually weighted, but are rather weighted by the amount ofcolor (saturation), and are offset to a mid-gray (half of white maximum)value. These U and V channels are thus not weighted perceptually by thegamma function, as is the Y (luminance) signal, or as are the R, G, andB values in coding systems which support direct RGB compression coding.

Thus, in another aspect of this invention, non-linear functions of R, G,and B, or of Y, U, and V, such as gamma or log, can be optionallyapplied when computing the difference values when creating an SNR layer.The difference values can then be processed using a function of themaximum difference, and or the average difference, knowing that thedifference itself is perceptually weighted to be approximatelyperceptually uniform.

Various alternatives are available to the typical video gamma ofapproximately 2.2. For example, a different gamma such as 2.6 or 1.8 canbe applied. Alternatively, a pure-logarithmic or a quasi-logarithmicnon-linear representation can be used to determine the difference(coding error) in a perceptually uniform manner for a given application.Other non-linear perceptually uniform representations can also be used,according to this invention, as described here.

Note that the use of floating-point representation for pixels andprocessing, including the quantization value, regional quantizationvalue, and/or scaled pixel values, allows precise control of these SNRweighting processes.

Relationship of SNR and Resolution Layers to Motion Compensation

As with SNR layers, motion compensation is intended to be applied tospecific layers, usually at medium to high resolution. Motioncompensation, in general, is the displacement of a region (usuallyrectangular or square) according to a motion vector (or more than onemotion vector, in the case of “B” frames), from a previous (orsubsequent) frame, for subtraction with a region in the current frame.

The motion compensated difference can then be band-split, quantized, andvariable-length-coded, as with intra (i.e. non-motion-compensated)frames. It is a property of motion compensation that decoding is appliedup to the base motion compensation resolution. Thus, no lower resolutiondecoding is available. Additional motion compensation can be applied atincreased resolutions, but all layers beyond the motion compensationlevel require the full decoding of the resolution at which the basemotion compensation is applied.

Note that SNR layers can be applied at any level at or below the basemotion compensation resolution. SNR and resolution-increasing layers canthen be applied at or above the base motion compensation resolution.However, only those SNR and resolution-increasing layers at or above thebase motion compensation resolution are optional. All SNR and resolutionlevels at or below the base motion compensation resolution are requiredfor every motion-compensated frame. This aspect of the invention is seenin FIG. 6, as described above.

Quantization as a Function of Layer

JPEG-2000 allows quantization to be specified for each subband layer.Another aspect of this invention is to improve layered transformquantization by utilizing finer overall quantization (by using lowerquantization parameter values) at lower resolution levels, inconjunction with regional quantization scaling. Since the lower levelsare at a lower resolution, each pixel in a lower level affects a numberof pixels in higher layers. In a deeply-layered band-split transformcoding system, a pixel in a lower resolution layer can affect manypixels in the higher resolution layers. There is also propagation ofinfluence at each layer due to the size of the filter kernels (which are9 or 7 taps in the DWT 9/7 system, but which may have any useful sizebetween two and dozens of taps). Thus, with each layer of increasingresolution, the influence of a pixel at a low resolution may propagateover an ever-widening and ever-heightening region.

In addition, the low-pass filter structure of band-split transforms ingeneral, and of the DWT 9/7 subband transform in particular, result inreduced noise at each lower resolution level. This is due to thestatistical reduction of noise which occurs when an image is low-passfiltered. The lower resolution layers should thus optimally be codedwith higher precision (via a lower quantization parameter), due not onlyto the large regional influence, but due also to the highersignal-to-noise inherent in the lower resolution image.

It is thus beneficial to optionally increase the precision ofquantization, via using an overall quantization parameter which becomessmaller at each lower resolution level during the band splittingtransform coded quantization, in conjunction with regional quantizationscaling.

Meeting Quality Targets

Using the tools described above, it is possible to statistically boundthe coding errors during image compression. The nature of computationalquantization as well as image transform quantization, yield astatistical variation in the accuracy of the coded result. However,using difference-controlled SNR layers, those regions having loweraccuracy can be specifically targeted for accuracy improvement. In thisway, nearly all of the pixel values can be brought within any definederror difference bound. This can be so effective that only a few pixelsout of millions may exceed this bound. It is then a simple matter tospecify the location of these few pixels, and the delta to apply tobring them to the intended value. In this way, every pixel within thedecoded image can be brought within any desired error bound.

The most useful method of determining the useful error bound is to takestatistics on the noise characteristics of the image. The noisegenerally is a function of brightness, and often of color as well. Oncethe noise floor is determined, for each frame, the error bound can beestablished. In general, some high-noise images contain useful picturedetail at a level below the noise floor. For such images, the noisebound could be usefully set to lower than the noise floor in order topreserve this sub-noise-floor picture detail. For low noise images, thenoise floor itself is likely to be a useful bound. When using a cameraas a high-accuracy sensor of the real world, using this methodologyallows confidence that the compression system is preserving all of theinformation provided by the sensor. This is important for someapplications, where the highest quality image is needed.

In addition to the full quality of image sensors or film-based imagescans, it is also often useful to provide lower quality images at ahigher compression ratio (less bits). Using the methodologies describedhere, lower quality layers can be provided which meet the need forhigher compression ratio, as well as additional layers which, whencombined with the lower quality layers, provide the entire qualityavailable in the original image. In this way, the coding system cancreate all of the data necessary to fully reproduce the original image,while having lower quality layers which simultaneously allow highercompression. Further, the higher compression layers contribute to thefull quality layers, such that little or no data is wasted. In somecases, such a layered structure is actually more efficient. This isespecially true of this invention, since the per-layer overhead has beenintentionally minimized to allow numerous layers with high efficiency.

Noise Characterization

As mentioned above, it is useful to have a characterization of the noisefloor of a still or moving image as a function of brightness and color.This can be easily accomplished using a color and grayscale chart, ifone was photographed and adjusted along with the rest of the scene(usually at the start of the scene). Such a chart usually containssquares of uniform colors and brightnesses. Each such square can then beused to determine the variation of one pixel to adjacent pixels(horizontally, vertically, and diagonally), since all pixels should seethe same brightness and colors within the uniform square. Further, oneframe to the next can be compared over a number of frames, to determinevariations with each frame, since each frame should also see the samecolor within each square. In this way, the frame-to-frame (temporal)noise, and the pixel-to-pixel (spatial) noise in the original image canbe directly observed for each color and brightness square.

In addition to these basic premises, there are a number of practicalconsiderations with respect to characterization of imaging systems.

If the chart is in focus, and dust or scratches are present on thesquares of color and brightness, there are unwanted variations. It isthus beneficial to slightly defocus the chart, to reduce these dust andscratch effects, as well as to reduce variations in the colored and graysquares themselves, which may use textured paint having inherent arealvariation.

Another practical issue involves silicon image sensors. For siliconsensors, and other types of electronic image sensors, it is common tohave manufacturing variation in pixel sensitivity for each pixellocation. This is called “fixed pattern noise”, since the variation doesnot move or change because it is attached to each pixel. Such noise isnot affected by whether the image is in focus, but is more easilyrevealed if the image is not in focus. It is best to average capturedimages over a number of frames in order to determine the fixed patternnoise at each pixel at each brightness. This can be done by imaging aset of full-frame colors and brightnesses, or by any other method ofensuring that each pixel location is tested at all brightness levels towhich the sensor is responsive. The average thus determined can then beremoved from every original image generated by that specific sensor byappropriate computation, such as subtraction.

Note that fixed pattern noise is interferes with motion-compensation,since the image is moving but the fixed pattern is not. Thus, it isanother beneficial aspect of this invention to remove fixed-patternnoise, when such characterization is possible, prior tomotion-compensated compression.

Another version of fixed-pattern-noise occurs with digitally-scannedfilm. The sensor is usually an area array, with identicalfixed-pattern-noise issues to electronic cameras, or alternatively ascanned line-array. In the case of the line array, thefixed-pattern-noise appears as vertical or horizontal stripes (dependingon the direction of scan). Again, it is beneficial to characterize andremove such noise prior to motion-compensated compression.

The removal of fixed pattern noise is also beneficial for high precisionintra (non-motion-compensated) compression, since the noise stripes orpixel pattern require coding bits, but do not represent imageinformation. It should be noted that digital cameras, area array, andline array scanners typical apply some correction to reduce the amountof fixed pattern noise. Thus, the discussion here applies to theremaining uncorrected fixed pattern noise.

Note that film perforation registration is imprecise, and differentialfilm shrinkage occurs. Scanned film therefore exhibits “gate weave” andother motion variations not inherent in the original image. Suchdistortions and unwanted motions are treated as image motion by thepresent invention, and the removal of these distortions and unwantedmotions is the subject of known techniques. If such distortions andunwanted motions are removed, the resulting image more closely matchesthe original image in steadiness and actual motion, assuming that thealgorithms applied function properly. Note, however, that the processeswhich attempt to restore the correct motion to the film image also moveand alter the fixed pattern noise, making its removal impossible. Thus,another aspect of this invention is the process whereby fixed patternnoise is reduced or removed before using other technologies to steadythe scanned film image, prior to compression with this invention.

Another practical problem occurs due to lens flare, where bright regionsof the image “spill” into darker regions. This problem can be eliminatedby the use of full-frames of color and/or brightness. The problem can bereduced by imaging a multi-color, multi-brightness chart using known“flare correction” techniques which attempt to model the lens flare of agiven lens, and then subtracting out the resulting brightness and colorinterference due to that len's flare.

Full Range Low Bands

Another aspect of this invention is the use of one or more full-rangelow bands, meaning that at least the lowest band of a band-splithierarchy utilizes floating-point representation to provide wide-rangeand high precision. All higher bands can be coded with quantizedintegers, which are decoded using dequantized floating point valueswhich form high bands for the synthesis of higher resolution bands. Theuse of OpenExr half 16-bit floating point representation provides arelatively compact way of directly storing these low bands, takingadvantage of the internal lossless “Piz” compression (Huffman,cluster-table, Haar-wavelet).

In the case of motion compensation, this floating point low bandrepresents the low band of the difference image after motioncompensation, and is thus inherently signed, even for all-positivepixels.

Note that a common quantization artifact of smoothly-varying low-noiseimage regions is contour-banding due to quantization. This contouringartifact is eliminated by the use of high precision floating-point (orfixed point) representation for the lowest image pixel band, sinceprecise fine gradients are essentially exactly reproduced. One way toconceive of this novel approach is to consider that the lowest band isnot quantized at all, but is rather kept at very high precision. Sincethe resolution of this lowest layer can be fairly low, the overallcompression ratio can still be relatively high, even though the low bandis being represented at high precision.

In addition to storing a low band of the intra or motion compensatedimage, quantization can also be controlled regionally by using a lowresolution floating-point quantization image. Such an image can becreated using the low bands of a band-split hierarchy, or it can becreated using minima, maxima, or any other of the criteria described inthis invention. The resolution of the quantization image data need notmatch any band-split image resolution, but rather only need be describedin terms of the mapping to be used when applying the quantization valuesto pixels in their corresponding region at each resolution level.

Since the quantization parameter need not have fine gradations to beuseful, most of the benefit of using a floating point representation'swide range can still be retained if low-order mantissa bits are maskedoff. This has little affect on the image quality, but allows theclustering in the cluster-table of Piz OpenExr compression to yield goodcompression. A few bits of mantissa, allowing four to eight quantizationcode steps per factor of two, retains fine quantization steps whileallowing good Piz OpenExr compression.

While the discussion here has used Piz compression and OpenExr as anexample implementation, other floating-point systems, or otherwide-range scaled fixed-point systems, and similar compression systems,can also be used with this invention. One novel concept of thisinvention is the use of a wide-range quantization image to regionallycontrol quantization. Another novel concept is the use of a wide-rangelow band with high precision representation, such as with a floatingpoint representation.

Yet another aspect of this invention is the use of alternatequantization control images for SNR bands and other high resolutionenhancing bands. The use of targeted SNR features can optionally utilizealternate quantization images, of varying useful resolutions, toregionally determine quantization parameters. One or more suchadditional quantization images can be used in conjunction with one ormore SNR or high-band resolution layers, as appropriate. Thequantization images provide a means to target specific features to bedecoded more finely, thereby placing SNR improvements orresolution-increasing detail in the most desirable locations.

Another way of obtaining wide dynamic range using low bands according tothis aspect of the invention is to utilize the quantized integers ofcommon practice, but to use a floating-point representation for thequantize/dequantize parameter. The floating point quantization parameterthen allows wide-range coding. For motion compensated coding, the rangeof floating point representation may only be needed on intra framequantization parameters, since the range of motion-compensateddifferences is small, even when the frames all have a wide range. Thus,an integer quantization parameter can be optionally used at any and alllayers, including the lowest, when using motion compensation, since widerange can be enabled entirely by a floating point quantization parameterfor the low intra band. Such a floating point quantization parameter maybe regionally applied at the resolution of its layer or at a lowerresolution, or a single value may be used for the entire layer, ifappropriate. Thus, this invention allows a mixture of integer andfloating point lowest band pixels in intra and motion compensatedframes, together with a mixture of floating point and fixed pointquantization parameters, both regional and per-layer. The system of thisinvention is being utilized as long as at least one low-bandquantization parameter and one low-band pixel image uses floating pointrepresentation.

Noise Dithering Prior to Quantization

When truncating to a lower number of bits, dithering is the process ofadding noise in order to reduce the visibility of adjacent brightnesssteps. When the actual image noise floor at each resolution layer isutilized to establish overall and/or regional quantization, the noisefloor itself properly dithers the image pixel values prior toquantization.

As another optional aspect of this invention, however, if a coarserquantization is used at one or more required or optional layers (forhigher compression at those layers), then it sometimes is beneficial todither the pixel values with pseudo-random noise prior to quantization.The sum of the added noise and the inherent noise should equal thequantization step size. The use of the floating point quantizationparameter of this invention is beneficial for precise control of suchdithering.

If an optional deadband is used around zero, then there are severaloptional treatments within the deadband. One treatment would add noiseat a level corresponding to all other band steps (i.e., those outsidethe deadband). This treatment would preserve the function of thedeadband, but dither some values to integer plus-or-minus one whichwould otherwise have been rounded to zero within the deadband. Anothertreatment is to add noise to values which would not be within thedeadband without dithering. Thus, the compression benefit of thedeadband would remain unaffected. Another treatment is to utilizedithering the size of the deadband, by adding half of the amount of thedeadband's extent between zero and the point where the deadband value isexceeded (which generates an integer one). Note that the sign of thetransformed pixel value is taken into account, such that half thedeadband is subtracted from negative values in this treatment. Othertreatments, as combinations of the above, or as a function of quantizedamount, are also potentially useful.

Note that low bands and high bands can be optionally selected for noisedithering, and the dithering level for each can differ. Ditheringgenerally increases the compressed bit-size, and thus the amount ofdithering is a tradeoff between image quality benefit and compressionefficiency. Thus, it is sometimes be useful to have the sum of theinherent local image noise and the added pseudo-random noise be lessthan the quantization stepsize, in order to reduce output bit-sizesomewhat, while gaining some of the benefit of dithering.

Note also that low bands of optional SNR layers are likely to benefitfrom different noise dithering treatments than optional SNR high bands.Further, optional SNR and resolution-enhancing bands may find differentdithering amounts useful, as well as no dithering, as a function of theband characteristics. Further, required resolution-enhancing high bands,if quantized at a level above their noise floor, may benefit fromspecific but differing (as a function of particular level) amounts ofdithering, and specific deadband treatments.

If information about the noise floor of a moving image and its regionsis not available, a generic guess of the overall image noise floor canbe used to help select the amount of dithering to apply when usingquantization parameters likely to substantially exceed the generic noisefloor guess.

As noted above with respect to this invention, the quantizationparameter is generally the multiplicative product of the overallquantization and the local region quantization scale, for each layer.Further, the image noise floor generally decreases with eachlower-resolution band, with a correspondingly finer (lower) quantizationparameter. Such quantization is sometimes targeted to focus bits atspecific features within the image at higher optional layers. The lowestlayer uses high precision floating point, having a precision set toexceed even the very lowest noise floor image sources, including manylayers of low pass filtering, such that the lowest band itself is neverquantized above the noise floor. Only bands above the lowest level (bothhigh and low bands) are candidates for pseudo-random noise dithering asdescribed here.

Adaptive Lossless Coding by Region

Once integers are obtained by dividing the quantization parameter intothe band-split-transformed pixel value, they are efficiently coded in alossless (bit-exact) manner. Common practice uses a singlevariable-length coding (“vlc”) table such as Huffman or Arithmeticcoding. With Huffman coding, each value is coded separately. WithArithmetic coding, multiple values are coded together. It is also commonpractice to extend a simple static table to multiple static tables, eachfor different purposes. Further, there exists a practice of “adaptive”variable length coding, where several tables are available, and the mostefficient one can be chosen and signaled to the decoder. It is alsopossible to send a table from the encoder to the decoder, based upon aspecific efficient coding for that data. These adaptive and signaledpractices are not common, but they have been utilized, sometimes with areasonable compression efficiency gain.

A companion to these variable-length coding practices is theirapplication to specific transform coding regions. For example, JPEG-2000uses the entire image at each resolution band. Also, for example,MPEG-1, MPEG-2 and MPEG-4 (both parts 2 and 10) code their integer DCTvalues using a choice of several patterns (e.g., scan or zig-zag),corresponding to each DCT block (e.g., 4×4, 8×8, 8×4, 16×16 pixels).

It is another novel aspect of this invention that the losslessvariable-length coding of band-split transform pixel coefficients arecoded using adaptive regions. These regions can have arbitrary size andshape, and are selected solely on lossless coding efficiency (i.e.,minimum bit-size). The size and shape of these regions is not limited toany particular size and shape, unlike previous vlc coding methods. Inthis aspect of the invention, a number of “vlc” tables are availablewhich are tested against all selected region sizes and shapes. Further,downloadable vlc tables can be utilized. For each selected region sizeand shape, all of the statically-available vlc tables are tested forbit-size. The size, shape, and vlc table which has the smallest bit-sizeare then signaled to the decoder.

Downloadable vlc tables can also be tested, usually in conjunction withspecific data patterns which benefit from vlc tables which optimizethese data patterns. Note that such downloadable vlc tables can becreated algorithmically, driven by the specifics of the data pattern. Ifsuch a downloadable pattern, at a specific size and shape, utilizes thesmallest bit-size, including the amortized table download size, thensuch a table is optionally be selected as most efficient.

While there are no restrictions on the size and shape of thevlc-lossless coding regions used in this aspect of the invention, simplerectangular regions are easiest to implement. Note that a wide range ofregion sizes, from as small as 2×2 (or 1×1) to as large as 128×128 (oreven larger), may be utilized to fully automate a wide range of data.Also, extreme rectangular shapes such as 2×128 and 128×2 is alsosometimes optimal, as well as any shape in-between, or sometimes evenmore extreme shapes.

For example, the SNR layers describe above, which target specificfeatures, may encounter large regions where only sparse data is needed.For such large regions, the ability to adapt the region sizes and shapes(using the best vlc for that region) to the need for coded data in thoseregions greatly reduces the bit-coding overhead of high-resolution SNRlayers. Using this aspect of the invention, many SNR layers can beapplied, even at very high resolution (such as 4 k resolution) with onlya small bit-size overhead for having such layers in regions in whichthey are not actively targeting image improvement. Further, if such anSNR layer finds little “work” to do, meaning when none of the targetedimage data is present, then there is little overhead for having thelayer present. This is a significant benefit to this adaptive regionsize, shape, and vlc selection method.

It is beneficial to target certain vlc coding tables to optimize forlarge regions of zero using a small number of bits for the region. It isalso beneficial to code transformed pixels individually, or in groups,where the maximum values are limited to a small range, such as ±1 or ±2.When a vlc is available which is limited to coding a small range, therange of values for the region can be simply tested via max and min, andthat vlc code need not be measured for that size and shape if the pixelvalues are beyond its range. Further, if all the values are zero in aspecific region size and shape, the zero coding is optimal, so no othervlc tables need be tested. In this way, an exhaustive optimum forminimum bit-size can be determined without requiring all vlc codings tobe calculated, since some codings can be simply eliminated.

Near the edges of the image, the coding region sizes and shapes arenaturally restricted. Further, when vlc tables are available whichcombine multiple pixels in a common code value, such vlc table optionscannot be considered if the available size at the edge does not fit.

While an optimized exhaustive method of trying all region sizes, shapes,and vlc options is described, it is also useful as an encoding speedoptimization to test only a subset of the remaining sizes, shapes, andvlc tables, based upon a sparse scan of the data to be coded. Using abest-guess of which sizes, shapes, and vlc tables best fit the sparsesampling, only those sizes, shapes, and vlc tables need be tested,eliminating the computations required to test all cases, even with theoptimizations described in the previous paragraph. For example, inaddition to the maximum and minimum, the average and the averagedeviation, standard deviation, or other statistical characterization canbe utilized to guess which vlc tables have the best chance of beingoptimal. Further, such statistical characterization can also eliminatesome vlc choices as being most likely to require more bits. Further,some region sizes and shapes can also be eliminated and others favoredfor some statistical characterizations. For example, sparse data is morelikely to benefit from large regions, whereas dense data is most likelyto benefit from small regions, since the vlc's for each region can beindependently optimized.

Using these methods of optimizing the choice of region size, shape, andvlc, only a small subset of choices need be tested during encoding, thusapproaching the minimum bit-size obtainable from exhaustive testingwhile reducing computation time.

Note that the adaptive use of coding region size, shape, and vlc needbear no relationship between one resolution layer and another, so thateach layer can be optimized independently of the others. Since thisaspect of this invention involves lossless bit-exact coding of integers(the integers result from the quantization division, fixed, or float),there is no effect on the visible image from the adaptive size, shape,and vlc table method described here. The use of adaptive coding regionsize and shape in conjunction with a choice of vlc tables is exclusivelyan optimization to minimizing the bit-size of the lossless coding ofaspects of the image, most notably the bandsplit-transform-coded pixelswhich form the bulk of image data. This aspect of the invention does notaffect integer values of the compression data nor the compressionquality, only the variable-length-coded bit-size of that data.

This adaptive lossless integer-coding method can also be applied tointeger quantization parameter sub-images, in the case wherequantization is regionally controlled by a low-resolution sub-image.Another aspect of the invention applies adaptive coding region size,shape, and vlc tables to low resolution floating-point low bands orfloating-point regional quantization images. In this way, floating-pointlow bands and quantization bands can be regionally grouped into regionsof similar exponent. In conjunction with quantization mantissatruncation to a few bits, such regional size, shape, and vlc coding canbe highly effective in coding regional low-resolution quantizationfloating point values. For low image bands of intra frames, the adaptivesize, shape, and vlc tables can also be beneficial, especially for theexponent and sign, although the higher required mantissa bit precisioninherently limits the amount of mantissa bit-size reduction achieved(unless mantissa precision is reduced when a high noise floor at thatlow band allows such reduction without reducing image quality).

Layered File Structure

Another aspect of this invention is the use of a file structure for thelayers of bits. Each resolution layer, each SNR layer, eachfloating-point lowest band, each quantization regional low band, and themotion compensation form different aspects of the bits generated duringcompression. It has proven convenient to organize the compressed bitsinto files, with one file for each frame for each aspect of that frame.Each aspect is further placed in a folder dedicated to that aspect. Theoverall project or scene contains all of these aspect folders within ahighest-level folder or storage device (such as a recordable digitaldisk like DVD-R or CD-R).

In addition, at the start of each sequence (typically a scene), as wellas at optional convenient subsequent frames, a header file can bestored. The header file contains typical header information or metadata,such as the number of frames, the distance between intra frames (ifmotion compensation is active, or a distance of “one” to deactivatemotion compensation), the resolution of each layer, the presence orabsence of each optional layer, the overall quantization values for eachlayer present, the size of the deadband for each layer, and otherrequired per-sequence information. It is usual to include a header filewhen inserting intra-frames at less frame distance than a specified“intra-stride” parameter value.

FIG. 8 is a diagram showing one possible layered file structure inaccordance with this aspect of the invention. Each entry in the exampleshown is in the form of a type identifier (“Folder” or “File”) and anitem name (e.g., “low-low_floating_point”); however, in implementation,the type identifier generally is implicit in the directory structure ofa computer file system. In this example, a root folder 800 includes oneor more header files 802 as described above, and then a series of folder804 representing a separable aspect of the compressed image sequence.Each folder 804 contains files 806 that contain the actual bitscorresponding to each frame of the specific separable aspect of thecompressed image sequence represented by the folder structure.

This folder and file organization has proven convenient in a number ofways. It is easy to select some layers, and not others, by only copyingthose folders containing the desired layers (as long as they providesufficiency for decoding at the desired level). Those folders containinglayers which are beyond the current need not be copied nor transmitted.At encoding and decoding time, it becomes simple to automaticallygenerate the folder and file names from the aspect and frame number.

This intermediate folder-per-aspect and file-per-frame-per-aspectstructure provides a convenient way to manage the many layers that are afeature of this invention.

This intermediate folder and file structure can also be read by atransmission processing code which can assemble the desired number oflayers into a typical compressed moving image stream multiplex. Themultiplex need only concatenate the bits from the per-frame files withinthe aspect folders, with a simple header to indicate which aspect blockfollows, and the length of that block of data. The selected multiplexmay be transmitted, or it may be stored, as with MPEG-2 and other commonmoving image compression systems.

It is straightforward for the decoder to decode the multiplex.Alternatively, the flexible intermediate aspect folder and filestructure can be reconstructed from the stored or transmittedmultiplexed bitstream using a corresponding demultiplex processing code.Such demultiplex code can reconstruct the aspect folder and filestructure from all of the layers and aspects which were included in themultiplex, or may optionally omit reconstruction of some optionalaspects which are not needed.

Automated Intra Selection

There have been numerous methods to decide when to insert an extraintra-frame into a sequence, usually by looking for a scene cut byexamining the pixels of adjacent frames. In this aspect of theinvention, an optional method of inserting new intra frames is to countthe number of bits needed for a motion compensated frame. If that numberis not at least a reasonable percent less than the bits needed for theprevious intra frame, then the motion-compensated frame can be discardedand an intra frame can be made. For example, an intra-insert can beselected if the motion compensated frame does not use at least 5% fewerbits (or any other useful amount, as specified by an adjustableparameter). Further, if desirable, a motion compensated frame compressedbit-size can be allowed which is larger than the previous intra framebit-size by a specified amount.

Note that motion compensation has overhead for motion vectors and motionmodes. When frames are significantly different, the motion compensationoverhead may cause the motion compensated frame to actually be largerthan the previous intra frame. Some other common cases, where the amountof data is very small, such as a fade-up from black, may result inmotion compensation overhead that is a large portion of the data in eachframe, and accordingly may automatically select an intra-insert forevery frame during the fade-up, solely based upon a comparison of thenumber of bits. For this purpose, the number of bits of all coded layerscan be used, or alternatively, the number of bits up to a lower usefullayer (excluding one or more optional higher resolution and SNR layers).

Note that the intra-coding of the current frame need not create the samenumber of bits as the previous reference intra frame. Thus, the choiceof a percentage (or alternatively, an absolute amount) difference isbeneficial when comparing between the motion compensated compressedbit-size of the current frame and the previous intra frame (which may bemany frames previous). The selection of the percentage (or other)difference amount can be altered to match the intra-frame stridedistance and the type of scene being compressed, in order to optimizethe use of this automatic intra-frame insert.

This simple bit-size comparison method has proven highly-effective inoptimizing the quality and reducing the number of coded bits during highscene change, fade-up, and other special cases.

Implementation

FIG. 9 is a block diagram showing one implementation of various aspectsof the invention in a non-motion compensated compression/decompressionsystem. Various stages of this implementation are optional, and otheroptional stages or steps may be included. A source image 900 isconverted to a floating point representation 902 (if not already in thatformat). The floating-point image 902 is subjected to a band-splitanalysis 904 having a floating point output. The resulting unquantizedlowest band 906 may be directly output in a floating pointrepresentation, such as the OpenExr format, as the lowest layer of thecompressed source image.

One or more of the remaining bands may be quantization scaled 908 bytaking into account regional minimum and average pixel values. Eachselected band-split image is then quantized 910 using an overallquantization parameter 912 and a regional quantization scale image 908,with all quantization parameters and quantization scales preferablyrepresented in floating point format. The integer output of thequantizer 910 can then be coded using an adaptive-region VLC code 914and output as coded bits 916 representing the high-band layers of thecompressed source image.

To generate SNR layers, the output of the quantizer 910 is dequantized918 to a floating point representation and up-filtered by a bandsynthesis filter 920. The output of the band synthesis filter 920 issubtracted 922 from the floating point source image 902 and subjected toa band-split analysis 924. The maximum error 926 for the output of thatanalysis can then be determined and used to generate quantization scaleimage 928. The quantization scale image 928 is used to regionally scalequantzation of the output of the band-split analysis 924 through aquantizer 930, coded using an adaptive-region VLC code 932, and outputas coded bits 934 representing an error-bounded SNR layer.

The output of the quantizer 930 may be dequantized 936 to a floatingpoint representation and subtracted 938 from the source image 900 to beused with one or more SNR targets 940, 942 to generate corresponding SNRdifference images 944, 946 as shown in FIG. 6.

The invention may be implemented in hardware or software, or acombination of both (e.g., programmable logic arrays). Unless otherwisespecified, the algorithms included as part of the invention are notinherently related to any particular computer or other apparatus. Inparticular, various general purpose machines may be used with programswritten in accordance with the teachings herein, or it may be moreconvenient to construct more specialized apparatus (e.g., integratedcircuits) to perform particular functions. Thus, the invention may beimplemented in one or more computer programs executing on one or moreprogrammable computer systems each comprising at least one processor, atleast one data storage system (including volatile and non-volatilememory and/or storage elements), at least one input device or port, andat least one output device or port. Program code is applied to inputdata to perform the functions described herein and generate outputinformation. The output information is applied to one or more outputdevices, in known fashion.

Each such program may be implemented in any desired computer language(including machine, assembly, or high level procedural, logical, orobject oriented programming languages) to communicate with a computersystem. In any case, the language may be a compiled or interpretedlanguage.

Each such computer program is preferably stored on or downloaded to astorage media or device (e.g., solid state memory or media, or magneticor optical media) readable by a general or special purpose programmablecomputer, for configuring and operating the computer when the storagemedia or device is read by the computer system to perform the proceduresdescribed herein. The inventive system may also be considered to beimplemented as a computer-readable storage medium, configured with acomputer program, where the storage medium so configured causes acomputer system to operate in a specific and predefined manner toperform the functions described herein.

Novel and useful aspects of the invention include at least thefollowing:

(1) Use of a different quantization parameter value at two or moreresolution levels of a coding transform resolution hierarchy, includinguse of reduced-resolution levels of a band-split or band-split hierarchyas regional scaling for quantization.

(a) Optionally applied to the bi-orthogonal subband 9/7 Discrete WaveletTransform resolution hierarchy.

(b) Optionally applied to a more optimal band-split hierarchy.

(c) Optionally including a minimum and/or maximum on thereduced-resolution quantization scale, optionally as a function of eachresolution level.

(2) Use of a floating-point quantization parameter value in conjunctionwith integer or floating point reduced-resolution levels of a band-splitor band-split hierarchy as regional scaling for the floating-pointquantization.

(3) Use of high precision, such as floating-point orhalf-floating-point, for a low resolution level of a coding transformhierarchy.

(a) Optionally including the bi-orthogonal subband 9/7 Discrete WaveletTransform resolution hierarchy.

(4) Use of floating point everywhere in an image compression codingbetween input (pixel input is optionally integer), up to the point ofthe quantization divide (after which coding is bit-exact lossless).Thus, all transform processing is performed in floating point.

(5) Similar use in the decoder of floating point everywhere, after thedequantization multiply, up to the final pixel values (which may beoptionally converted to integers as a final step).

(6) Use of actual image (intra-style) low bands for quantization, butapplying quantization not only to intra-coding, but also (when motioncompensated) to motion-compensated difference bands (including bands atdifferent levels, higher and/or lower, of the band-split hierarchy).

(7) Use of a resolution hierarchy, wherein the minimum value is takenfor a suitable low-low band, for use as a scale factor for thequantization parameter for all pixels at higher resolution levels;

(a) Optionally including use of specific minimum and/or maximum values,optionally as a function of each resolution level.

(b) Optionally including adjacent pixel overlap at each level, to takeinto account the influence of the band-split filter extent.

(8) Use of resulting coding error difference values to create a newsuitable low-low band for use in weighting quantization for an SNRcorrection layer such that larger errors have smaller quantizationparameter values (thus higher number of correction bits generated).

(a) Optionally including adjacent pixel overlap at each level, to takeinto account the influence of the band-split filter extent.

(b) Optionally including an additional quantization weighting as afunction of the amount of factor that a given pixel's error is above theaverage error

(9) Use of a different deadband quantization zone at two or moreresolution levels of a coding transform resolution hierarchy.

(a) Optionally applied to the bi-orthogonal subband 9/7 Discrete WaveletTransform resolution hierarchy.

(10) Use of noise dithering at the point of quantization, with the noiselevel being set at the quantization stepsize or smaller. When setsmaller, the goal is for the sum of the inherent image noise (at thatlevel) plus the added noise, to equal the quantization step. Thedeadband (around zero) noise dithering may optionally be treateddifferently than other steps.

(11) Use of a statistical numeric characterization, such as a transformamplitude histogram as a function of one or more of color, brightness,and region for one or more SNR layers in order to reproduce theappearance of film grain or camera noise.

(12) Use of the count of bits generated by a motion compensated frame,in relative comparison to the count of bits within the previous intraframe, to determine whether to recompute the motion compensated frame asan intra frame instead.

(13) Use of adaptive region sizes to optimize for minimum bits whenlossless bit coding using one or more choices of variable-length codingtables.

(14) Use of an optional intermediate file folder structure to groupcompressed data components for various layers.

A number of embodiments of the invention have been described.Nevertheless, it is understood that various modifications may be madewithout departing from the spirit and scope of the invention. Forexample, some of the steps described above may be order independent, andthus can be performed in an order different from that described.Accordingly, other embodiments are within the scope of the followingclaims.

The invention claimed is:
 1. A computer-implemented method forcompressing a sequence of digitized video images represented as pixelsutilizing signal-to-noise improvement layers, the method beingimplemented in a computer system comprising one or more physicalprocessors, the method comprising: generating at least one processedimage layer from at least one of the sequence of digitized video images,each processed image layer comprising a plurality of values representinga coded image layer; and generating a plurality of signal-to-noiselayers, each signal-to-noise layer being derived from a correspondingcoded image layer, wherein at least one such signal-to-noise layer istargeted to improving the quality of a selected specific feature of thecorresponding coded image layer.
 2. The method of claim 1, wherein atleast one generated processed image layer is a band-split layer.
 3. Themethod of claim 1, further including encoding at least one generatedprocessed image layer by preferentially allocating coding bits toregions of interest in such at least one generated processed image layerby decreasing the amplitude of pixel differences near edges of thecorresponding digitized video image using a weighting which decreases asa function of nearness to such image edges.
 4. The method of claim 1,wherein generating at least one processed image layer includes applyinga filter structure to generate a hierarchy of reduced resolution layersfrom one of the sequence of digitized video images.
 5. The method ofclaim 4, further including generating at least one signal-to-noise layercorresponding to at least one of the hierarchy of reduced resolutionlayers.
 6. The method of claim 1, wherein the pixels of the sequence ofdigitized video images have values representing different colors withina color space, and further including: generating at least one processedimage layer by applying a non-linear function to each color value of thepixels of at least one of the sequence of digitized video images tocreate a perceptually weighted image; and generating a signal-to-noiselayer from each perceptually weighted image.
 7. The method of claim 6,wherein the non-linear function is a gamma function.
 8. The method ofclaim 6, wherein the non-linear function is a log function.
 9. Acomputer-implemented method for compressing a sequence of digitizedvideo images represented as pixels utilizing signal-to-noise improvementlayers, the method being implemented in a computer system comprising oneor more physical processors, the method comprising: generating at leastone processed image layer from at least one of the sequence of digitizedvideo images, wherein each processed image layer comprises a pluralityof values representing one of a coded image layer or a signal-to-noiselayer derived from a coded image layer; and encoding at least onegenerated processed image layer by preferentially allocating coding bitsto regions of interest in such generated processed image layer bydecreasing the amplitude of pixel differences near edges of thecorresponding digitized video image using a weighting which decreases asa function of nearness to such image edges.
 10. The method of claim 9,wherein generating at least one processed image layer includes applyinga filter structure to generate a hierarchy of reduced resolution layersfrom one of the sequence of digitized video images.
 11. The method ofclaim 10, further including generating at least one signal-to-noiselayer, each such signal-to-noise layer corresponding to one of thehierarchy of reduced resolution layers.
 12. The method of claim 9,wherein the pixels of the sequence of digitized video images have valuesrepresenting different colors within a color space, and whereingenerating at least one processed image layer includes: applying anon-linear function to each color value of the pixels of at least one ofthe sequence of digitized video images to create a perceptually weightedimage; and generating a signal-to-noise layer from each perceptuallyweighted image.
 13. The method of claim 12, wherein the non-linearfunction is a gamma function.
 14. The method of claim 12, wherein thenon-linear function is a log function.
 15. The method of claim 9,wherein at least one generated processed image layer is a band-splitlayer.